Neural Information Processing Systems http://nips.cc/

With high-dimensional state spaces, visual reinforcement learning (RL) faces significant challenges in exploitation and exploration, resulting in low sample efficiency and training stability.

However, such a global learning rate schedule does not take into account the structural characteristics of neural networks (NNs).

We summarize our contributions as follows: 1.

However, existing approaches to DTs often struggle to generalize to unseen conditions in data-scarce settings, a crucial requirement for such models. To address these limitations, our work begins by establishing the essential desiderata for effective DTs.